Global heterogeneous graph convolutional network: from coarse to refined land cover and land use segmentation

Abstract

ABSTRACTThe abundant details embedded in very-high-resolution remote sensing images establish a solid foundation for comprehending the land surface. Simultaneously, as spatial resolution advances, there is a corresponding escalation in the required granularity of land cover and land use (LCLU) categories. The coarse classes identified necessitate further refinement into more detailed categories. For instance, the ‘built-up’ class can be subdivided into specific categories such as squares, stadiums, and airports. These refined LCLU classifications are better equipped to support diverse domains. Nonetheless, most studies simply adopt methods initially designed for coarse LCLU when addressing the challenging refined LCLU segmentation. Few studies have considered the inherent relationships between coarse and refined LCLU, overlooking the potential exploitation of the numerous recently released LCLU products. To better leverage this prior knowledge, we propose the Global Heterogeneous Graph Convolutional Network (GHGCN). The GHGCN introduces a heterogeneous graph and excels in establishing relationships between coarse and refined LCLU, which can extract long-distance dependencies more effectively than convolutional neural networks. Furthermore, this model is performed end-to-end, eliminating the necessity for presegmentation and facilitating training acceleration. GHGCN exhibits competitive performance compared to state-of-the-art models, indicating its effective design in exploiting coarse LCLU data, especially for categories with limited samples. The source code is released at: https://github.com/Liuzhizhiooo/GHGCN.

Keywords

• Land cover and land use (LCLU)
• semantic segmentation
• heterogeneous graph
• graph convolutional network (GCN)